Signature forgery is a widespread problem throughout the world, wherein the signatures of different people may be forged with a varying probability of success. In particular, there are three commonly accepted signature fraud scenarios, including: (1) random forgery; (2) casual forgery; and (3) skillful forgery. Random forgery occurs when the forger knows neither the victim's name nor the victim's authentic signature. Consequently, the fraudulent signature will likely have no relation to the authentic one. For example, the forger may sign using a signature other than that of the victim. Instances of random forgery are easy to detect using conventional “off-line” or “static” signature validation technology.
Casual forgery occurs when the forger knows the victim's name, but not the victim's signature. As a result, the fraudulent signature is in the name of the victim and therefore is likely to have some relation the victim's authentic signature. When dealing with casual forgery, the chances of producing a fraudulent signature that looks similar to the authentic one depends to a great extent on how close the authentic signature matches the person's name, which may be referred to herein in terms of “signature simplicity”. Skillful forgery occurs when the forger knows the victim's name and signature, and may reproduce the signature with a relatively high resemblance to the authentic signature. Conventional “off-line” or “static” signature validation technology typically cannot detect forgery that falls in the skillful category.
In view of the above drawbacks, there exists a need for a method for applying a signature simplicity analysis for improving the accuracy of signature validation.